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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Loan Eligibility Prediction System |
| Authors Name: | Chandan Singh Palhania , Amit Kumar Jaiswal , Gaurav Kumar , Utkarsh Raj , Shekhar Rana |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_191320
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| Published Paper Id: | IJNRD2304328 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | In the modern financial system, banks give firms or people looking to buy anything the necessary initial investment. to assess a borrower’s creditworthiness and forecast the possibility that they will be granted a loan. For lenders, banks, and financial organisations, a loan eligibility prediction system can be helpful in automating the loan application process and determining the risk of giving money to a certain applicant. It is a piece of software that uses techniques for data analysis and machine learning. The system includes compiling data on sanctioned loans and loan applications from a variety of sources. The data contains facts on the borrower’s income, job history, debt-to-income ratio, loan amount, loan period, and other relevant information. The data is then prepared for use in the machine’s training by being cleaned, preprocessed, and transformed. Then, relevant traits that can influence loan eligibility are identified from the data. This entails creating new factors or changing the ones already in use to predict loan eligibility. Following the division of the data into train and test sets, a machine learning model is selected and trained from different algorithms that are available. The testing set is used to evaluate the model’s performance after it has been trained on the training set. After the method for predicting loan eligibility is created, it can be incorporated into a programme that banks and lenders can use to determine loan eligibility. The loan eligibility decisionmaking process should be well explained in the application, which should also be easy to use. To make sure the model is reliable and useful over time, the loan eligibility prediction system should be constantly reviewed and updated with fresh data. In conclusion, a loan eligibility prediction system will be a useful tool for banks, financial institutions, and lenders to automate the application process and determine the risk involved in giving money to a certain borrower. The system entails gathering, pre-processing, and manipulating data; extracting pertinent features; choosing an appropriate machine learning model; training the model; and implementing it in a lending and banking application. |
| Keywords: | Loan eligibility prediction, machine learning |
| Cite Article: | "Loan Eligibility Prediction System", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.d201-d205, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304328.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2304328 Registration ID: 191320 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: d201-d205 Country: MOHALI, Panjab , India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304328 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304328 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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